This notebook contains a set of analyses for analyzing DicedOut’s boardgamegeek collection. The bulk of the analysis is focused on building a user-specific predictive model to predict the games that the specified user is likely to own. This enables us to ask questions like, based on the games the user currently owns, what games are a good fit for their collection? What upcoming games are they likely to purchase?
We can look at a basic description of the number of games that the user owns, has rated, has previously owned, etc.
What years has the user owned/rated games from? While we can’t see when a user added or removed a game from their collection, we can look at their collection by the years in which their games were published.
We can look at the most frequent types of categories, mechanics, designers, and artists that appear in a user’s collection.
We’ll examine predictive models trained on a user’s collection for games published through 2020. How many games has the user owned/rated/played in the training set (games prior to 2020)?
username | dataset | period | games_owned | games_rated |
DicedOut | training | published before 2020 | 235 | 292 |
DicedOut | validation | published 2020 | 7 | 3 |
DicedOut | test | published after 2020 | 1 | 1 |
The main outcome we will be modeling for the user is owned, which refers to whether the user currently owns or has a previously owned a game in their collection. Our goal is to train a predictive model to learn the probability that a user will add a game to their collection based on its observable features. This amounts to looking at historical data and looking to find patterns that exist between features of games and games present in the user’s collection.
One of the models we trained was a decision tree, which looks for decision rules that can be used to separate games the user owns from games they don’t. The resulting model produces a decision corresponding to yes or no statements: to explain why the model predicts the user to own game, we start at the top of the tree and follow the rules that were learned from the training data.
Note: the tree below has been further pruned to make it easier to visualize.
Decision trees are highly interpretible models that are easy to train and can identify important interactions and nonlinearities present in the data. Individual trees have the drawback of being less predictive than other common models, but it can be useful to look at them to gain some understanding of key predictors and relationships found in the training data.
We can examine coefficients from another model we trained, which is a logistic regression with elastic net regularization (which I will refer to as a penalized logistic regression). Positive values indicate that a feature increases a user’s probability of owning/rating a game, while negative values indicate a feature decreases the probability. To be precise, the coefficients indicate the effect of a particular feature on the log-odds of a user owning a game.
Why did the model identify these features? We can make density plots of the important features for predicting whether the user owned a game. Blue indicates the density for games owned by the user, while grey indicates the density for games not owned by the user.
Binary predictors can be difficult to see with this visualization, so we can also directly examine the percentage of games in a user’s collection with a predictor vs the percentage of all games with that predictor.
% of Games with Feature | ||||
username | Feature | User_Collection | All_Games | Ratio |
DicedOut | Pandasaurus Games | 2.1% | 0.2% | 10.95 |
DicedOut | Asmodee | 20.9% | 2.4% | 8.61 |
DicedOut | Pegasus Spiele | 12.8% | 2.1% | 6.05 |
DicedOut | Wizards Of The Coast | 2.1% | 0.5% | 3.89 |
DicedOut | Realtime Game | 10.2% | 3.4% | 3.01 |
DicedOut | Word Game | 6.4% | 2.2% | 2.88 |
DicedOut | Deduction Game | 13.6% | 5.0% | 2.70 |
DicedOut | Kosmos | 5.1% | 2.0% | 2.61 |
DicedOut | Party Game | 19.6% | 9.2% | 2.12 |
DicedOut | Card Game | 40.0% | 29.4% | 1.36 |
DicedOut | Dice Rolling | 22.1% | 28.4% | 0.78 |
DicedOut | Childrens Game | 1.7% | 8.0% | 0.21 |
DicedOut | Secret Unit Deployment | 0.4% | 2.9% | 0.15 |
DicedOut | Wargame | 0.4% | 18.9% | 0.02 |
DicedOut | Movement Points | 0.0% | 2.4% | 0.00 |
DicedOut | Travel | 0.0% | 1.1% | 0.00 |
Before predicting games in upcoming years, we can examine how well the model did and what games it liked in the training set. In this case, we used resampling techniques (cross validation) to ensure that the model had not seen a game before making its predictions.
Displaying the 100 games from the training set with the highest probability of ownership, highlighting in blue games the user has owned.
Rank | Published | ID | Name | Pr(Owned) | Owned |
1 | 2019 | 270971 | Era: Medieval Age | 0.952 | no |
2 | 2019 | 286096 | Tapestry | 0.921 | yes |
3 | 2019 | 281946 | Aftermath | 0.839 | no |
4 | 2018 | 205896 | Rising Sun | 0.825 | no |
5 | 2017 | 174430 | Gloomhaven | 0.821 | no |
6 | 2017 | 220775 | Codenames: Disney – Family Edition | 0.814 | no |
7 | 2016 | 205158 | Codenames: Deep Undercover | 0.754 | no |
8 | 2019 | 265736 | Tiny Towns | 0.732 | yes |
9 | 2018 | 199792 | Everdell | 0.728 | no |
10 | 2016 | 169786 | Scythe | 0.684 | no |
11 | 2019 | 283863 | The Magnificent | 0.678 | no |
12 | 2011 | 70919 | Takenoko | 0.664 | yes |
13 | 2011 | 96848 | Mage Knight Board Game | 0.651 | no |
14 | 2009 | 39683 | At the Gates of Loyang | 0.626 | no |
15 | 2008 | 37380 | Roll Through the Ages: The Bronze Age | 0.599 | no |
16 | 2010 | 82702 | Funfair | 0.588 | no |
17 | 2015 | 178900 | Codenames | 0.578 | yes |
18 | 2014 | 157354 | Five Tribes | 0.569 | no |
19 | 2012 | 123096 | Space Cadets | 0.563 | no |
20 | 2007 | 31260 | Agricola | 0.553 | yes |
21 | 2017 | 224037 | Codenames: Duet | 0.547 | yes |
22 | 2010 | 73439 | Troyes | 0.543 | no |
23 | 2016 | 205398 | Citadels | 0.525 | no |
24 | 2017 | 230059 | Crossfire | 0.516 | no |
25 | 2008 | 38453 | Space Alert | 0.512 | yes |
26 | 2017 | 233078 | Twilight Imperium: Fourth Edition | 0.486 | no |
27 | 2019 | 244099 | Herbaceous Sprouts | 0.482 | no |
28 | 2016 | 171131 | Captain Sonar | 0.479 | no |
29 | 2016 | 205637 | Arkham Horror: The Card Game | 0.474 | no |
30 | 2015 | 175878 | 504 | 0.468 | no |
31 | 2013 | 143693 | Glass Road | 0.456 | yes |
32 | 2005 | 18723 | Aye, Dark Overlord! The Red Box | 0.453 | no |
33 | 2013 | 143741 | BANG! The Dice Game | 0.452 | no |
34 | 2010 | 65200 | Asteroyds | 0.446 | no |
35 | 2019 | 285984 | Last Bastion | 0.440 | no |
36 | 2012 | 124708 | Mice and Mystics | 0.439 | no |
37 | 2016 | 176083 | Hit Z Road | 0.437 | no |
38 | 1993 | 1234 | Once Upon a Time: The Storytelling Card Game | 0.429 | no |
39 | 2016 | 198454 | When I Dream | 0.426 | yes |
40 | 2003 | 6824 | Gold und Rum | 0.424 | no |
41 | 2018 | 247236 | Duelosaur Island | 0.413 | no |
42 | 2012 | 104710 | Wiz-War (Eighth Edition) | 0.412 | no |
43 | 2016 | 198773 | Codenames: Pictures | 0.411 | yes |
44 | 2014 | 159508 | AquaSphere | 0.408 | no |
45 | 2019 | 266192 | Wingspan | 0.405 | yes |
46 | 2014 | 163412 | Patchwork | 0.405 | yes |
47 | 2013 | 127024 | Room 25 | 0.396 | no |
48 | 2012 | 121921 | Robinson Crusoe: Adventures on the Cursed Island | 0.395 | no |
49 | 2012 | 124742 | Android: Netrunner | 0.392 | no |
50 | 2004 | 9209 | Ticket to Ride | 0.371 | yes |
51 | 2018 | 257321 | Gen7: A Crossroads Game | 0.366 | no |
52 | 2019 | 259081 | Machi Koro Legacy | 0.360 | no |
53 | 2004 | 9220 | Saboteur | 0.355 | no |
54 | 2018 | 245638 | Coimbra | 0.345 | no |
55 | 2016 | 200680 | Agricola (Revised Edition) | 0.343 | no |
56 | 2017 | 219215 | Werewords | 0.343 | no |
57 | 2016 | 167791 | Terraforming Mars | 0.335 | no |
58 | 2017 | 220774 | Codenames: Marvel | 0.335 | no |
59 | 2010 | 66362 | Glen More | 0.334 | no |
60 | 2017 | 197376 | Charterstone | 0.322 | no |
61 | 2005 | 15062 | Shadows over Camelot | 0.321 | no |
62 | 2019 | 275089 | Mental Blocks | 0.315 | no |
63 | 2017 | 162886 | Spirit Island | 0.312 | no |
64 | 2000 | 478 | Citadels | 0.305 | yes |
65 | 2012 | 129622 | Love Letter | 0.304 | yes |
66 | 2009 | 40692 | Small World | 0.300 | yes |
67 | 2005 | 14996 | Ticket to Ride: Europe | 0.299 | yes |
68 | 2010 | 70512 | Luna | 0.298 | no |
69 | 2016 | 156858 | Black Orchestra | 0.296 | no |
70 | 2017 | 229220 | Santa Maria | 0.294 | no |
71 | 2007 | 31481 | Galaxy Trucker | 0.285 | yes |
72 | 2017 | 195539 | The Godfather: Corleone's Empire | 0.284 | no |
73 | 2017 | 216658 | Smash Up: What Were We Thinking? | 0.284 | no |
74 | 2016 | 193037 | Dead of Winter: The Long Night | 0.280 | no |
75 | 2015 | 175549 | Salem 1692 | 0.278 | no |
76 | 2002 | 4098 | Age of Steam | 0.278 | no |
77 | 2016 | 198487 | Smash Up: Cease and Desist | 0.273 | no |
78 | 2014 | 150376 | Dead of Winter: A Crossroads Game | 0.273 | yes |
79 | 2013 | 133848 | Euphoria: Build a Better Dystopia | 0.272 | no |
80 | 2019 | 283294 | Yukon Airways | 0.269 | no |
81 | 2012 | 113294 | Escape: The Curse of the Temple | 0.267 | yes |
82 | 2001 | 1345 | Genoa | 0.267 | no |
83 | 1997 | 11 | Bohnanza | 0.265 | yes |
84 | 2014 | 132531 | Roll for the Galaxy | 0.264 | no |
85 | 2019 | 276894 | Ticket to Ride: London | 0.264 | no |
86 | 2012 | 119391 | Il Vecchio | 0.263 | no |
87 | 1997 | 42 | Tigris & Euphrates | 0.260 | no |
88 | 1992 | 118 | Modern Art | 0.260 | yes |
89 | 2019 | 280789 | Pandemic: Rapid Response | 0.258 | no |
90 | 1999 | 88 | Torres | 0.253 | no |
91 | 2013 | 146278 | Tash-Kalar: Arena of Legends | 0.250 | no |
92 | 2011 | 69552 | Panic Station | 0.250 | no |
93 | 2017 | 227789 | Heaven & Ale | 0.249 | no |
94 | 2018 | 244711 | Newton | 0.244 | no |
95 | 2008 | 33107 | Senji | 0.244 | no |
96 | 2015 | 181304 | Mysterium | 0.242 | yes |
97 | 1998 | 503 | Through the Desert | 0.240 | no |
98 | 2017 | 225244 | Ticket to Ride: Germany | 0.235 | no |
99 | 2011 | 84876 | The Castles of Burgundy | 0.233 | no |
100 | 2014 | 157809 | Nations: The Dice Game | 0.231 | no |
This section contains a variety of visualizations and metrics for assessing the performance of the model(s) during resampling. If you’re not particularly interested in predictive modeling, skip down further to the predictions from the model.
An easy way to examine the performance of classification model is to view a separation plot. We plot the predicted probabilities from the model for every game (from resampling) from lowest to highest. We then overlay a blue line for any game that the user does own. A good classifier is one that is able to separate the blue (games owned by the user) from the white (games not owned by the user), with most of the blue occurring at the highest probabilities (right side of the chart).
We can more formally assess how well each model did in resampling by looking at the area under the receiver operating characteristic curve. A perfect model would receive a score of 1, while a model that cannot predict the outcome will default to a score of 0.5. The extent to which something is a good score depends on the setting, but generally anything in the .8 to .9 range is very good while the .7 to .8 range is perfectly acceptable.
wflow_id | .metric | .estimator | .estimate |
GLM | roc_auc | binary | 0.89 |
Decision Tree | roc_auc | binary | 0.75 |
Another way to think about the model performance is to view its lift, or its ability to detect the positive outcomes over that of a null model. High lift indicates the model can much more quickly find all of the positive outcomes (in this case, games owned or played by the user), while a model with no lift is no better than random guessing. A gains chart is another way to view this.
While we are probably more interested in the lift provided by the models to evaluate their efficacy, we can also explore the optimal cutpoint if we wanted to define a hard threshold for identifying games a user will own vs not own.
The threshold we select depends on how we much we care about false positives (games the model predicts that the user does not own) vs false negatives (games the user owns that the model does not predict). We can toggle threshold to
Finally, we can understand the performance of the model by examining its calibration. If the model assigns a probability of 5%, how often does the outcome actually occur? A well calibrated model is one in which the predicted probabilities reflect the probabilities we would observe in the actual data. We can assess the calibration of a model by grouping its predictions into bins and assessing how often we observe the outcome versus how often our model expects to observe the outcome.
A model that is well calibrated will closely follow the dashed line - its expected probabilities match that of the observed probabilities. A model that consistently underestimates the probability of the event will be over this dashed line, be a while a model that overestimates the probability will be under the dashed line.
What games does the model think DicedOut is most likely to own that are not in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2019 | 270971 | Era: Medieval Age | 0.952 | no |
2019 | 281946 | Aftermath | 0.839 | no |
2018 | 205896 | Rising Sun | 0.825 | no |
2017 | 174430 | Gloomhaven | 0.821 | no |
2017 | 220775 | Codenames: Disney – Family Edition | 0.814 | no |
What games does the model think DicedOut is least likely to own that are in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2016 | 177524 | ICECOOL | 0.002 | yes |
2017 | 201921 | Tiny Epic Quest | 0.003 | yes |
2012 | 130185 | Speedy Recall | 0.003 | yes |
2017 | 220778 | Sticky Chameleons | 0.003 | yes |
2015 | 195137 | Catacombs (Third Edition) | 0.004 | yes |
Top 25 games most likely to be owned by the user in each year, highlighting in blue the games that the user has owned.
rank | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
1 | Space Cadets | Glass Road | Five Tribes | Codenames | Codenames: Deep Undercover | Gloomhaven | Rising Sun | Era: Medieval Age |
2 | Mice and Mystics | BANG! The Dice Game | AquaSphere | 504 | Scythe | Codenames: Disney – Family Edition | Everdell | Tapestry |
3 | Wiz-War (Eighth Edition) | Room 25 | Patchwork | Salem 1692 | Citadels | Codenames: Duet | Duelosaur Island | Aftermath |
4 | Robinson Crusoe: Adventures on the Cursed Island | Euphoria: Build a Better Dystopia | Dead of Winter: A Crossroads Game | Mysterium | Captain Sonar | Crossfire | Gen7: A Crossroads Game | Tiny Towns |
5 | Android: Netrunner | Tash-Kalar: Arena of Legends | Roll for the Galaxy | Blood Rage | Arkham Horror: The Card Game | Twilight Imperium: Fourth Edition | Coimbra | The Magnificent |
6 | Love Letter | Heads Up!: Party Game | Nations: The Dice Game | The Game | Hit Z Road | Werewords | Newton | Herbaceous Sprouts |
7 | Escape: The Curse of the Temple | Ladies & Gentlemen | Ultimate Werewolf | Tiny Epic Galaxies | When I Dream | Codenames: Marvel | Between Two Castles of Mad King Ludwig | Last Bastion |
8 | Il Vecchio | Caverna: The Cave Farmers | Hook! | Between Two Cities | Codenames: Pictures | Charterstone | Neon Gods | Wingspan |
9 | Archipelago | Rory's Story Cubes: Prehistoria | Akrotiri | Viticulture Essential Edition | Agricola (Revised Edition) | Spirit Island | The World of SMOG: Rise of Moloch | Machi Koro Legacy |
10 | Suburbia | Viticulture | Fields of Arle | Pirates of the 7 Seas | Terraforming Mars | Santa Maria | The Grizzled: Armistice Edition | Mental Blocks |
11 | Dixit Jinx | Forbidden Desert | La Granja | Mombasa | Black Orchestra | The Godfather: Corleone's Empire | Arkham Horror (Third Edition) | Yukon Airways |
12 | Terra Mystica | Time 'n' Space | King of New York | RallyRas | Dead of Winter: The Long Night | Smash Up: What Were We Thinking? | Railroad Ink: Deep Blue Edition | Ticket to Ride: London |
13 | Zombicide | Patchistory | Onirim (Second Edition) | Sylvion | Smash Up: Cease and Desist | Heaven & Ale | Cosmic Run: Regeneration | Pandemic: Rapid Response |
14 | Yedo | Lewis & Clark: The Expedition | Imperial Settlers | Through the Ages: A New Story of Civilization | Greedy Greedy Goblins | Ticket to Ride: Germany | Sunset Over Water | The Only Word: the Party Word Game |
15 | The Resistance: Avalon | City of Remnants | Port Royal | Heroes | Honshū | Sagrada | Nyctophobia | Marvel Champions: The Card Game |
16 | Agricola: All Creatures Big and Small | Amerigo | The Worst Game Ever | Unusual Suspects | Bloodborne: The Card Game | Santo Domingo | Ticket to Ride: New York | Sierra West |
17 | Sky Tango | Impulse | Camel Up | Die Fiesen 7 | Aeon's End | Pandemic Legacy: Season 2 | Reykholt | Amul |
18 | Dixit: Journey | BioShock Infinite: The Siege of Columbia | Deception: Murder in Hong Kong | Imagine | Agricola: Family Edition | This War of Mine: The Board Game | Decrypto | Century: A New World |
19 | Shadows over Camelot: The Card Game | Legacy: The Testament of Duke de Crecy | Colt Express | Aye, Dark Overlord! The Green Box | Star Trek: Frontiers | Dinosaur Island | Shadows: Amsterdam | Silver & Gold |
20 | Seasons | Two Rooms and a Boom | One Night Ultimate Werewolf | GEM | Mansions of Madness: Second Edition | Secrets | KeyForge: Call of the Archons | One Key |
21 | Smash Up | Cappuccino | Orléans | Smash Up: Munchkin | Ticket to Ride: Rails & Sails | The Castles of Burgundy: The Dice Game | Rebel Nox | Maracaibo |
22 | Ghost Blitz 2 | Spyrium | Subdivision | One Night Ultimate Werewolf: Daybreak | Star Wars: Rebellion | Muse | Azul: Stained Glass of Sintra | Codenames: The Simpsons |
23 | Keyflower | Hanamikoji | Sheriff of Nottingham | Treasure Hunter | Dream Home | Pandemic: Rising Tide | Renegade | Paladins of the West Kingdom |
24 | New Amsterdam | Concept | Spyfall | Mission: Red Planet (Second Edition) | Cottage Garden | Breaking Bad: The Board Game | Muse: Awakenings | Comanauts |
25 | Clash of Cultures | Funemployed | Roll Through the Ages: The Iron Age | 7 Wonders Duel | Junk Art | Unfinished Case of Holmes | Pandemic: Fall of Rome | Villagers |
This is an interactive table for the model’s predictions for the training set (from resampling).
We’ll validate the model by looking at its predictions for games published in 2020. That is, how well did a model trained on a user’s collection through 2020 perform in predicting games for the user in 2020?
username | outcome | dataset | method | .metric | .estimate |
DicedOut | owned | validation | GLM | roc_auc | 0.818 |
DicedOut | owned | validation | Decision Tree | roc_auc | 0.730 |
Table of top 50 games from 2020, highlighting games that the user owns.
Published | ID | Name | Pr(Owned) | Owned |
2020 | 291457 | Gloomhaven: Jaws of the Lion | 0.364 | no |
2020 | 300322 | Hallertau | 0.333 | no |
2020 | 312804 | Pendulum | 0.272 | yes |
2020 | 296626 | Sonora | 0.242 | no |
2020 | 316412 | The LOOP | 0.228 | no |
2020 | 256317 | Guild Master | 0.212 | no |
2020 | 318983 | Faiyum | 0.203 | no |
2020 | 295486 | My City | 0.197 | no |
2020 | 309113 | Ticket to Ride: Amsterdam | 0.191 | no |
2020 | 316377 | 7 Wonders (Second Edition) | 0.174 | no |
2020 | 295687 | Trust Me, I'm a Doctor | 0.173 | no |
2020 | 298371 | Wild Space | 0.173 | no |
2020 | 301716 | Glasgow | 0.165 | no |
2020 | 306481 | Tawantinsuyu: The Inca Empire | 0.159 | no |
2020 | 301767 | Mysterium Park | 0.157 | no |
2020 | 256999 | Project: ELITE | 0.148 | no |
2020 | 284217 | Rush M.D. | 0.138 | no |
2020 | 303669 | Magic Rabbit | 0.134 | no |
2020 | 292333 | Cowboys II: Cowboys & Indians Edition | 0.128 | no |
2020 | 261403 | Inhuman Conditions | 0.122 | no |
2020 | 282081 | The Zorro Dice Game | 0.118 | no |
2020 | 316750 | The Princess Bride Adventure Book Game | 0.115 | no |
2020 | 293141 | King of Tokyo: Dark Edition | 0.111 | no |
2020 | 302425 | Unlock!: Mythic Adventures | 0.110 | no |
2020 | 318084 | Furnace | 0.108 | no |
2020 | 296892 | Sacred Rites | 0.107 | no |
2020 | 299592 | Beez | 0.106 | no |
2020 | 302723 | Forgotten Waters | 0.100 | yes |
2020 | 314040 | Pandemic Legacy: Season 0 | 0.099 | no |
2020 | 301919 | Pandemic: Hot Zone – North America | 0.098 | no |
2020 | 246900 | Eclipse: Second Dawn for the Galaxy | 0.092 | no |
2020 | 295905 | Cosmic Frog | 0.092 | no |
2020 | 184267 | On Mars | 0.086 | no |
2020 | 296512 | The Game: Quick & Easy | 0.084 | no |
2020 | 233262 | Tidal Blades: Heroes of the Reef | 0.084 | no |
2020 | 306735 | Under Falling Skies | 0.084 | no |
2020 | 306040 | Merv: The Heart of the Silk Road | 0.083 | no |
2020 | 313817 | Hello Neighbor: The Secret Neighbor Party Game | 0.079 | no |
2020 | 304679 | Ctrl | 0.078 | no |
2020 | 304420 | Bonfire | 0.078 | no |
2020 | 298638 | Sheriff of Nottingham: 2nd Edition | 0.076 | no |
2020 | 310442 | Feierabend | 0.075 | no |
2020 | 308765 | Praga Caput Regni | 0.074 | no |
2020 | 300327 | The Castles of Tuscany | 0.073 | no |
2020 | 301880 | Raiders of Scythia | 0.073 | no |
2020 | 316554 | Dune: Imperium | 0.073 | no |
2020 | 262208 | Dungeon Drop | 0.072 | no |
2020 | 309630 | Small World of Warcraft | 0.072 | no |
2020 | 316622 | Gods Love Dinosaurs | 0.072 | no |
2020 | 311927 | Long Live the King: A Game of Secrecy and Subterfuge | 0.071 | no |
We can then refit our model to the training and validation set in order to predict all upcoming games for the user.
Examine the top 100 upcoming games, highlighting in blue ones the user already owns.
Published | ID | Name | Pr(Owned) | Owned |
2021 | 285967 | Ankh: Gods of Egypt | 0.379 | no |
2022 | 349067 | The Lord of the Rings: The Card Game – Revised Core Set | 0.322 | no |
2021 | 336794 | Galaxy Trucker | 0.280 | no |
2021 | 339906 | The Hunger | 0.231 | no |
2021 | 326804 | Rorschach | 0.230 | no |
2021 | 332944 | Sobek: 2 Players | 0.230 | no |
2021 | 340237 | Wonder Book | 0.224 | no |
2021 | 343905 | Boonlake | 0.220 | no |
2021 | 329465 | Red Rising | 0.212 | no |
2021 | 333553 | For the King (and Me) | 0.192 | no |
2021 | 305682 | Rolling Realms | 0.191 | no |
2022 | 356033 | Libertalia: Winds of Galecrest | 0.181 | no |
2022 | 304051 | Creature Comforts | 0.167 | no |
2022 | 349793 | Age of Rome | 0.167 | no |
2021 | 331635 | Kameloot | 0.163 | no |
2021 | 298102 | Roll Camera!: The Filmmaking Board Game | 0.162 | no |
2022 | 317511 | Tindaya | 0.160 | no |
2021 | 316287 | Quest | 0.135 | no |
2022 | 322524 | Bardsung | 0.130 | no |
2022 | 310873 | Carnegie | 0.127 | no |
2022 | 331106 | The Witcher: Old World | 0.126 | no |
2021 | 339789 | Welcome to the Moon | 0.125 | no |
2021 | 273330 | Bloodborne: The Board Game | 0.122 | no |
2021 | 249277 | Brazil: Imperial | 0.118 | no |
2021 | 314088 | Agropolis | 0.115 | no |
2021 | 332386 | Brew | 0.115 | no |
2021 | 340909 | Gloomholdin' | 0.112 | no |
2021 | 344258 | That Time You Killed Me | 0.112 | no |
2021 | 308989 | Bristol 1350 | 0.111 | no |
2021 | 325698 | Juicy Fruits | 0.103 | no |
2021 | 290236 | Canvas | 0.097 | no |
2023 | 347909 | Rogue Angels: Legacy of the Burning Suns | 0.095 | no |
2021 | 339790 | Cocktail | 0.094 | no |
2022 | 295770 | Frosthaven | 0.090 | no |
2021 | 304783 | Hadrian's Wall | 0.085 | no |
2021 | 331685 | Hit the Silk! | 0.085 | no |
2021 | 306169 | MATCH 5 | 0.085 | no |
2021 | 339484 | Savannah Park | 0.084 | no |
2021 | 340677 | Bad Company | 0.084 | no |
2022 | 251661 | Oathsworn: Into the Deepwood | 0.083 | no |
2021 | 347304 | Time's Up!: Harry Potter | 0.083 | no |
2021 | 343696 | Dune: Betrayal | 0.082 | no |
2021 | 291572 | Oath: Chronicles of Empire and Exile | 0.079 | no |
2021 | 342848 | World of Warcraft: Wrath of the Lich King | 0.078 | no |
2021 | 342942 | Ark Nova | 0.075 | no |
2021 | 338834 | MicroMacro: Crime City – Full House | 0.074 | no |
2021 | 283242 | The Whatnot Cabinet | 0.074 | no |
2021 | 291859 | Riftforce | 0.071 | no |
2021 | 313730 | Harsh Shadows | 0.069 | no |
2021 | 338980 | Eastern Empires | 0.066 | no |
2021 | 304336 | Million Dollar Script | 0.064 | no |
2021 | 314491 | Meadow | 0.064 | no |
2022 | 314580 | Hamburg | 0.064 | no |
2022 | 332393 | Bridge City Poker | 0.064 | no |
2021 | 277700 | Merchants Cove | 0.063 | no |
2021 | 344408 | Full Throttle! | 0.063 | no |
2021 | 339263 | Summoner Wars (Second Edition): Starter Set | 0.063 | no |
2021 | 335541 | We Care: a Grizzled Game | 0.063 | no |
2021 | 332800 | Summoner Wars (Second Edition) | 0.062 | no |
2022 | 351605 | Bohnanza: 25th Anniversary Edition | 0.061 | no |
2021 | 298069 | Cubitos | 0.061 | no |
2021 | 221298 | NewSpeak | 0.060 | no |
2021 | 300523 | Biblios: Quill and Parchment | 0.060 | no |
2021 | 340466 | Unfathomable | 0.059 | no |
2022 | 340672 | Council of 12 | 0.058 | no |
2021 | 295947 | Cascadia | 0.057 | no |
2021 | 328286 | Mission ISS | 0.057 | no |
2021 | 340455 | King of the Valley | 0.056 | no |
2022 | 308028 | Drop Drive | 0.056 | no |
2021 | 344277 | Corrosion | 0.054 | no |
2022 | 311988 | Frostpunk: The Board Game | 0.053 | no |
2021 | 300305 | Nanga Parbat | 0.052 | no |
2022 | 342900 | Earthborne Rangers | 0.052 | no |
2021 | 337262 | Fangs | 0.052 | no |
2021 | 332290 | Stardew Valley: The Board Game | 0.051 | no |
2022 | 320718 | Hidden Leaders | 0.051 | no |
2022 | 271601 | Feed the Kraken | 0.051 | no |
2022 | 283137 | Human Punishment: The Beginning | 0.050 | no |
2021 | 315937 | X-Men: Mutant Insurrection | 0.050 | no |
2021 | 328569 | Mint Bid | 0.049 | no |
2021 | 329962 | Cantaloop: Book 2 – A Hack of a Plan | 0.048 | no |
2021 | 291847 | Mantis Falls | 0.047 | no |
2021 | 320960 | Roll In One | 0.046 | no |
2021 | 348461 | Castle Break | 0.046 | no |
2021 | 339905 | Love Letter: Princess Princess Ever After | 0.045 | no |
2022 | 346199 | A Game of Thrones: B'Twixt | 0.045 | no |
2021 | 329670 | Pandemic: Hot Zone – Europe | 0.044 | no |
2021 | 307862 | Dollars to Donuts | 0.044 | no |
2021 | 316080 | KeyForge: Dark Tidings | 0.044 | no |
2021 | 311920 | Ultimate Werewolf: Extreme | 0.044 | no |
2021 | 337389 | Snakesss | 0.044 | no |
2021 | 306202 | Philosophia: Floating World | 0.044 | no |
2021 | 336552 | Mystic Paths | 0.043 | no |
2021 | 322622 | Floriferous | 0.043 | no |
2021 | 316786 | Tabannusi: Builders of Ur | 0.043 | no |
2021 | 328479 | Living Forest | 0.043 | no |
2021 | 256680 | Return to Dark Tower | 0.042 | no |
2021 | 340364 | Machi Koro 2 | 0.041 | no |
2021 | 319792 | Fly-A-Way | 0.041 | no |
2022 | 273814 | Deliverance | 0.041 | no |